Radio2Speech: High Quality Speech Recovery from Radio Frequency Signals

TL;DR

Radio2Speech leverages RF signals and advanced neural networks to recover high-quality speech, outperforming traditional microphone-based methods especially in noisy or soundproof environments.

Contribution

The paper introduces Radio2Speech, a novel system that uses RF signals and neural networks to recover high-quality speech, surpassing prior single-tone or incomprehensible speech recovery approaches.

Findings

Achieves state-of-the-art speech recovery in various environments

Performs comparably to microphones in quiet conditions

Effective in noisy and soundproof scenarios

Abstract

Considering the microphone is easily affected by noise and soundproof materials, the radio frequency (RF) signal is a promising candidate to recover audio as it is immune to noise and can traverse many soundproof objects. In this paper, we introduce Radio2Speech, a system that uses RF signals to recover high quality speech from the loudspeaker. Radio2Speech can recover speech comparable to the quality of the microphone, advancing from recovering only single tone music or incomprehensible speech in existing approaches. We use Radio UNet to accurately recover speech in time-frequency domain from RF signals with limited frequency band. Also, we incorporate the neural vocoder to synthesize the speech waveform from the estimated time-frequency representation without using the contaminated phase. Quantitative and qualitative evaluations show that in quiet, noisy and soundproof scenarios, Radio2Speech achieves state-of-the-art performance and is on par with the microphone that works in quiet scenarios.